A Quick Review of ML Algorithms

G, Chitralekha; Roogi, Jyoti M

doi:10.1109/icces51350.2021.9488982

Cited by 3 publications

References 12 publications

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Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

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Nowadays, the dependency on high-performance digital mobile connectivity is not limited to human usage but also the intelligent objects increasingly deployed to serve the needs of Internet of Things (IoT) applications. However, the current network planning technique limitation has constrained the real potential of mobile digital connectivity development. This situation has hindered sustainable Internet-oriented economic and technological development. The 3 rd generation partnership project (3GPP), through its specification release 18 (Rel.18), has included and leveraged the potential capabilities of machine learning (ML) technologies in advanced mobile network planning. The main objective is to enhance mobile network planning performance and reduce complexity. To materialize this aim, we propose a novel ML-based Online coverage Estimator (MLOE) tool developed based on Random Forest (RF) ML algorithm. It uses seven unique features to predict the mobile network performance through reference signal received power (RSRP). Accordingly, the results showed that MLOE outperformed traditional empirical techniques and previous works. The final trained RF algorithm has achieved an outstanding root mean square error (RMSE) of 2.65 dB and a coefficient of determination (𝑅𝑅 2 ) of 0.93. With the dynamic and fast-growing mobile technology, MLOE has been deployed on an online platform using MATLAB ® Web App Server, which offers a modular and scalable architecture.

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Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

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Design Thinking Approaches to Industrial Sales Forecasting with Advanced Data Analytical Tool

2023

2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)

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A Comprehensive Review of Internet-of-Things (IoT) Botnet Detection Techniques

Nketia,

Yaokumah,

Appati

2024

Smart and Agile Cybersecurity for IoT and IIoT Environments

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This chapter conducts a comprehensive empirical review of internet of things (IoT) botnet detection to identify gaps in the literature. An empirical analysis of literature work related to IoT botnet detection is conducted. A state-of-the-art review of works done on IoT botnet detection is synthesized. This review is based on classifying the subcategories of IoT botnet detection, including honeypot and intrusion detection techniques, specifically host and network-based IDSs. This is further broken down into anomaly, signature, and hybrid-based approaches. Anomaly-based detections include machine learning techniques and deep learning techniques. Other detection methods include distributed techniques (software defined networking [SDN] and blockchain), graph theory approach, and domain name service (DNS) techniques. Finally, the chapter recommends future research directions in IoT security and the application of deep learning techniques.

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A Quick Review of ML Algorithms

Cited by 3 publications

References 12 publications

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Design Thinking Approaches to Industrial Sales Forecasting with Advanced Data Analytical Tool

A Comprehensive Review of Internet-of-Things (IoT) Botnet Detection Techniques

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